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Three-dimensional radiomics of triple-negative breast cancer: Prediction of systemic recurrence.

ABSTRACT: This paper evaluated 3-dimensional radiomics features of breast magnetic resonance imaging (MRI) as prognostic factors for predicting systemic recurrence in triple-negative breast cancer (TNBC) and validated the results with a different MRI scanner. The Rad score was generated from 3-dimensional radiomic features of MRI for 231 TNBCs (training set (GE scanner), n?=?182; validation set (Philips scanner), n?=?49). The Clinical and Rad models to predict systemic recurrence were built up and the models were externally validated. In the training set, the Rad score was significantly higher in the group with systemic recurrence (median, -8.430) than the group without (median, -9.873, P?

SUBMITTER: Koh J

PROVIDER: S-EPMC7031504 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Three-dimensional radiomics of triple-negative breast cancer: Prediction of systemic recurrence.

Koh Jieun J Lee Eunjung E Han Kyunghwa K Kim Sujeong S Kim Dong-Kyu DK Kwak Jin Young JY Yoon Jung Hyun JH Moon Hee Jung HJ

Scientific reports 20200219 1

This paper evaluated 3-dimensional radiomics features of breast magnetic resonance imaging (MRI) as prognostic factors for predicting systemic recurrence in triple-negative breast cancer (TNBC) and validated the results with a different MRI scanner. The Rad score was generated from 3-dimensional radiomic features of MRI for 231 TNBCs (training set (GE scanner), n = 182; validation set (Philips scanner), n = 49). The Clinical and Rad models to predict systemic recurrence were built up and the mod ...[more]

PMID: 32076078

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Project description:Purpose To determine if a radiomics model based on quantitative maps acquired with synthetic MRI (SyMRI) is useful for predicting neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC). Materials and Methods In this prospective study, 181 women diagnosed with stage I-III TNBC were scanned with a SyMRI sequence at baseline and at midtreatment (after four cycles of NAST), producing T1, T2, and proton density (PD) maps. Histopathologic analysis at surgery was used to determine pathologic complete response (pCR) or non-pCR status. From three-dimensional tumor contours drawn on the three maps, 310 histogram and textural features were extracted, resulting in 930 features per scan. Radiomic features were compared between pCR and non-pCR groups by using Wilcoxon rank sum test. To build a multivariable predictive model, logistic regression with elastic net regularization and cross-validation was performed for texture feature selection using 119 participants (median age, 52 years [range, 26-77 years]). An independent testing cohort of 62 participants (median age, 48 years [range, 23-74 years]) was used to evaluate and compare the models by area under the receiver operating characteristic curve (AUC). Results Univariable analysis identified 15 T1, 10 T2, and 12 PD radiomic features at midtreatment that predicted pCR with an AUC greater than 0.70 in both the training and testing cohorts. Multivariable radiomics models of maps acquired at midtreatment demonstrated superior performance over those acquired at baseline, achieving AUCs as high as 0.78 and 0.72 in the training and testing cohorts, respectively. Conclusion SyMRI-based radiomic features acquired at midtreatment are potentially useful for identifying early NAST responders in TNBC. Keywords: MR Imaging, Breast, Outcomes Analysis ClinicalTrials.gov registration no. NCT02276443 Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Houser and Rapelyea in this issue.

Project description:Triple-negative breast cancer (TNBC), an intrinsic subtype of breast cancer, is characterized by aggressive pathology and shorter overall survival. Yet there is no effective therapy for these patients. Breast cancer stem cells (BCSCs) in TNBC may account for treatment failure. It is urgent to identify new therapeutic targets for TNBC. tRNA-derived small noncoding RNAs (tDRs) represent a new class of small noncoding RNAs (sncRNA), which have been reported in some human diseases and biological processes. However, there is no detailed information about the relationship between tDRs and BCSCs. In this study, a population of CD44+ /CD24-/low cells was isolated and identified by reliable BCSC surface markers. tDR expression profiles in TNBC and non-TNBC CSCs were performed by RNA sequencing. A total of 1327 differentially expressed tDRs contained 18 upregulated and 54 downregulated in TNBC group. Furthermore, the selected tDRs were validated by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). tDR-000620 expression level was consistently lower in TNBC cell lines CSCs (all P < 0.05) and serum samples (t = 2.597, P = 0.013). tDR-000620 expression was significant association with age (P = 0.018), node status (P = 0.026) and local recurrence (P = 0.019) by chi-square test. Kaplan-Meier method with log-rank test for comparison of recurrence curves. The results showed that the tDR-000620 expression (P = 0.002) and node status (P = 0.001) group were statistically significant with recurrence-free survival. Furthermore, multivariate Cox regression demonstrated that lymphatic metastasis (HR, 3.616; 95% CI, 1.234-10.596; P = 0.019) and low tDR-000620 expression (HR, 0.265; 95% CI, 0.073-0.959; P = 0.043) were two independent adverse predictive factors for recurrence-free survival. Finally, we found that tDR-000620 participated in some important biological processes though GO and KEGG analysis. Taken together, our study reveals the expression profiles of tDRs in TNBC and non-TNBC CSCs. It offers helpful information to understand the tDR-000620 expression is responsible for the aggressive phenotype of BCSCs. It may provide predictive biomarkers and therapeutic targets for TNBC recurrence.

Dataset Information

Three-dimensional radiomics of triple-negative breast cancer: Prediction of systemic recurrence.

Publications

Three-dimensional radiomics of triple-negative breast cancer: Prediction of systemic recurrence.

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